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arxiv: 2606.08705 · v1 · pith:644ZWFGAnew · submitted 2026-06-07 · 💻 cs.CL

Analyzing the Correlation Between Hallucinations and Knowledge Conflicts in Large Language Models

Lucrezia Laraspata , Giovanna Castellano , Gennaro Vessio This is my paper

Pith reviewed 2026-06-27 18:42 UTC · model grok-4.3

classification 💻 cs.CL
keywords hallucinationsknowledge conflictslarge language modelsprobingmodel interpretabilityactivation analysisLLaMAFalcon
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The pith

Hallucination activation patterns in LLMs cannot be fully reduced to knowledge conflict representations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether internal signals tied to knowledge conflicts inside large language models also explain why the models produce hallucinations. Researchers applied probing methods to layer activations and output logits in LLaMA-3-8B and Falcon-7B while running the models on hallucination detection tasks and knowledge-conflict datasets. They found the two kinds of internal patterns are conceptually linked yet remain distinct enough that one cannot account for the other. Probing still worked reliably across languages and layer types, suggesting it can help map different failure modes separately. This separation matters because it implies that fixing knowledge conflicts alone will not eliminate all hallucinations.

Core claim

Although conceptually related, hallucination activation patterns cannot be fully reduced to or explained by knowledge conflict representations. Probing across hidden, attention, and MLP layers plus output logits on LLaMA-3-8B and Falcon-7B shows the representations stay distinguishable even when the tasks overlap.

What carries the argument

Probing techniques applied to hidden, attention, and MLP layer activations together with output logits to compare representations of hallucinations and knowledge conflicts.

If this is right

  • Distinct internal mechanisms imply that mitigation strategies must target hallucinations and knowledge conflicts separately.
  • Probing remains effective for interpretability work even when the two phenomena do not collapse into one.
  • Fine-grained layer-wise analysis can continue to separate failure modes across languages and model sizes.
  • Training data conflicts alone do not fully determine hallucination behavior during inference.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the distinction holds, editing only conflicting facts in the training set will leave some hallucination pathways untouched.
  • Future work could test whether the same separation appears when models are asked to resolve conflicts at inference time rather than in static probes.
  • The result suggests that interpretability tools should track multiple distinct error signatures instead of assuming a single underlying cause.

Load-bearing premise

The chosen probing methods accurately isolate and separate the internal representations that belong to knowledge conflicts from those that belong to hallucinations.

What would settle it

A follow-up experiment that applies a new set of probes or a different architecture and finds that the same hallucination and conflict tasks now produce statistically indistinguishable activation patterns.

Figures

Figures reproduced from arXiv: 2606.08705 by Gennaro Vessio, Giovanna Castellano, Lucrezia Laraspata.

Figure 1
Figure 1. Figure 1: Accuracy and AUROC of probing models on detecting knowledge conflicts based on the activations of LLaMA-3-8B. The results were obtained by reproducing the original experiments presented in [6]. Probing results for hidden, attention, and MLP activations are shown in red, green, and blue, respectively. 0 5 10 15 20 25 30 Layer 0.56 0.58 0.60 0.62 0.64 0.66 0.68 Accuracy activation attention mlp (a) Accuracy … view at source ↗
Figure 2
Figure 2. Figure 2: Accuracy and AUROC of probing models on detecting hallucinations based on the activations of Falcon-7B. The results were obtained by reproducing the original experiments presented in [7]. Probing results for attention and MLP activations are shown in green and blue, respectively. internal generation process can encode signals of hallucination. Specifically, their method involves probing LLMs at various lay… view at source ↗
Figure 3
Figure 3. Figure 3: Probing hallucinations through knowledge conflicts. Each hallucination-related dataset is processed using LLaMA-3-8B, extracting hidden, attention, and MLP activations. These are fed into dedicated probing classifiers trained to predict knowledge conflict (KC) labels, which are then compared against the dataset￾provided hallucination labels. 3.4.1. From Knowledge Conflicts to Hallucinations To probe whethe… view at source ↗
Figure 4
Figure 4. Figure 4: Probing knowledge conflicts through hallucinations. The NQ-Swap dataset is processed using Falcon￾7B, from which output logits and intermediate activations (attention and MLP layers) are extracted. These are passed to dedicated probing classifiers to predict hallucination labels, which are compared with the ground-truth knowledge conflict labels. all layers. These features were then passed to the probing m… view at source ↗
Figure 5
Figure 5. Figure 5: Accuracy and AUROC of knowledge conflict probing models on detecting hallucinations based on the activations of LLaMA-3-8B. The probing results for hidden, attention, and MLP activations are colored red, green, and blue, respectively. setting, probing models trained on knowledge conflict-related activations appear largely ineffective at predicting hallucinations. This suggests that, at least in this contex… view at source ↗
Figure 6
Figure 6. Figure 6: Accuracy and AUROC of hallucination probing models on detecting knowledge conflicts based on the activations of Falcon-7B. The probing results for attention and MLP activations are colored green and blue, respectively. 0 5 10 15 20 25 30 Hidden Layer 0.0 0.2 0.4 0.6 0.8 Accuracy IT SV AR ZH HI FR EN ES DE FI EU CA FA CS (a) Hidden activations 0 5 10 15 20 25 30 Attn Layer 0.0 0.2 0.4 0.6 0.8 1.0 Accuracy I… view at source ↗
Figure 7
Figure 7. Figure 7: Accuracy of knowledge conflict probing models in detecting hallucinations across 14 languages, based on LLaMA-3-8B activations. Probing results are derived from the Mu-SHROOM dataset. 4.3. Probing Method Robustness To evaluate the robustness of the probing method from a general point of view, we analyze the performances of knowledge conflict probing models applied to the Mu-SHROOM dataset (since it was the… view at source ↗
read the original abstract

Hallucinations -- factually incorrect or unverifiable outputs -- remain one of the most challenging limitations of Large Language Models (LLMs), especially in knowledge-intensive tasks. One proposed explanation is internal knowledge conflicts arising from fixed, outdated training data. This paper investigates whether internal representations linked to knowledge conflicts correlate with hallucination behaviors in LLMs. Using probing techniques inspired by two prior works, we analyzed activations from hidden, attention, and MLP layers, as well as output logits, across predefined tasks. We probed LLaMA-3-8B on hallucination detection benchmarks and Falcon-7B on a knowledge conflict dataset. Our findings show that, although conceptually related, hallucination activation patterns cannot be fully reduced to or explained by knowledge conflict representations. Nonetheless, probing proves a robust tool across multiple languages and activation types, supporting its role in improving LLM interpretability. This work advances the broader understanding of hallucinations in LLMs and underscores the value of fine-grained analysis of their internal behavior.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that although hallucinations and knowledge conflicts are conceptually related in LLMs, their associated activation patterns cannot be fully reduced to or explained by each other. This is based on probing hidden, attention, and MLP layer activations plus output logits from LLaMA-3-8B on hallucination detection benchmarks and from Falcon-7B on a knowledge conflict dataset, with additional claims about probing robustness across languages and activation types.

Significance. If substantiated with appropriate controls, the result would indicate that hallucinations involve internal mechanisms distinct from knowledge conflicts, supporting targeted interpretability work. The multi-layer and multilingual probing approach would be a methodological strength if the cross-phenomenon comparison is valid.

major comments (2)
  1. [Abstract] Abstract: the central claim that hallucination activation patterns cannot be fully reduced to knowledge conflict representations requires evidence from within the same model and representational space, yet the study probes LLaMA-3-8B on one task and Falcon-7B on the other; any observed distinction could arise from differences in scale, architecture, pretraining, or tokenizer rather than the phenomena themselves.
  2. [Abstract / Methods] Experimental setup (inferred from abstract): no details are provided on how activations are aligned or compared across models, on statistical tests for reduction, or on controls for model-specific effects, which is load-bearing for the claim that patterns 'cannot be fully reduced'.
minor comments (1)
  1. [Abstract] The abstract states the models and tasks but does not clarify whether any cross-model alignment or shared-model control was performed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for highlighting these methodological concerns, which directly impact the strength of our central claim. We agree that the cross-model design introduces confounds and that details on comparisons were insufficiently specified. Below we respond point-by-point and indicate planned revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that hallucination activation patterns cannot be fully reduced to knowledge conflict representations requires evidence from within the same model and representational space, yet the study probes LLaMA-3-8B on one task and Falcon-7B on the other; any observed distinction could arise from differences in scale, architecture, pretraining, or tokenizer rather than the phenomena themselves.

    Authors: We agree that the use of distinct models (LLaMA-3-8B for hallucination benchmarks and Falcon-7B for the knowledge conflict dataset) prevents a direct comparison within a shared representational space and leaves open the possibility that observed differences stem from model-specific factors rather than the phenomena. Our model choices were dictated by the datasets employed in the cited prior works, but this does not resolve the issue. In revision we will qualify the abstract and discussion to present the non-reducibility finding as suggestive evidence across models rather than a definitive within-model result, and we will add an explicit limitations paragraph on this point. revision: partial

  2. Referee: [Abstract / Methods] Experimental setup (inferred from abstract): no details are provided on how activations are aligned or compared across models, on statistical tests for reduction, or on controls for model-specific effects, which is load-bearing for the claim that patterns 'cannot be fully reduced'.

    Authors: The full Methods section describes layer-wise activation extraction (hidden states, attention, MLP) and logit probing, yet we acknowledge that cross-model alignment procedures, the precise statistical tests used to evaluate reduction (e.g., correlation or accuracy-difference metrics), and explicit controls for model-specific effects were not detailed. We will expand the Methods and add an appendix specifying these elements, including any normalization steps and the quantitative criteria applied to assess whether hallucination patterns are reducible to knowledge-conflict representations. revision: yes

Circularity Check

0 steps flagged

Empirical probing study shows no definitional or self-referential circularity

full rationale

The paper conducts an empirical analysis by applying probing techniques to activations in LLaMA-3-8B (hallucination task) and Falcon-7B (knowledge conflict task), concluding that patterns are not fully reducible. No equations, fitted parameters renamed as predictions, or self-citation chains that bear the central load are described. The derivation relies on observed activation differences rather than any input being redefined as output. Minor self-citation risk exists only in the 'inspired by prior works' phrasing, but this does not reduce the claim by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No details on free parameters, axioms, or invented entities are provided in the abstract.

pith-pipeline@v0.9.1-grok · 5707 in / 818 out tokens · 25756 ms · 2026-06-27T18:42:29.346742+00:00 · methodology

discussion (0)

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Reference graph

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